To efficiently compress a time variable video sequence, redundancy in the temporal domain as well as in the two dimensional spatial domain must be reduced. MPEG uses a discrete cosine transform (DCT) to reduce the redundancy in the two dimensional spatial domain and a motion compensation method to reduce the redundancy in the temporal domain.
The DCT is a method of reducing the correlativity between data through a two dimensional spatial transformation. Each block in a picture is spatially transformed using the DCT after the picture is divided into blocks. Data that has been spatially transformed tends to be driven to a certain direction. Only a group of the data driven in the certain direction is quantized and transmitted.
Pictures, which are consecutive in the temporal domain, form motions of a human being or an object at the center of the frame. This property is used to reduce the redundancy of the temporal domain in the motion compensation method. A volume of data to be transmitted can be minimized by taking out a similar region from the preceding picture to fill a corresponding region, which has not been changed (or has very little change), in the present picture. The operation of finding the most similar blocks between pictures is called a motion estimation. The displacement representing a degree of motion is called a motion vector. MPEG uses a motion compensation—DCT method so that the two methods combine.
When a compression technique is combined with a DCT algorithm, the DCT transform is usually performed after input data is sampled in a unit size of 8×8, and the transform coefficients are quantized with respect to a visual property using quantization values from a quantization table. Then, the data is compressed through a run length coding (RLC). The data processed with the DCT is converted from a spatial domain to a frequency domain and compressed through the quantization with respect to the visual property of human beings, not to be visually recognized. For example, since eyes of human beings are insensitive to a high frequency, a high frequency coefficient is quantized in a large step size. Thus, a quantization table is made according to external parameters, such as a display characteristic, watching distance, and noise, to perform an appropriate quantization.
For the quantized data, the data having a relatively high frequency is coded with a short code word. The quantized data having a low frequency is coded with a long code word. Thus, the data is finally compressed.
In processing a moving picture as discussed above, blocks are individually processed to maximize the compression ratio and coding efficiency. However, the individual process causes blocking artifacts that disturb the eyes of human beings at boundaries between blocks.
Accordingly, various methods for reducing a blocking artifact in a coding system, which individually processes blocks, are presented. For example, attempts to reduce the blocking artifact by changing processes of coding and decoding have been implemented. However, this method of changing the processes of coding and decoding increases the amount of bits to be transmitted.
Another method for reducing the blocking artifact is based on the theory of projection onto convex sets (POCS). However, this method is applied to only a still picture because of an iteration structure and convergence time.
The blocking artifact is a serious problem in a low transmit rate moving picture compression. Since a real-time operation is necessary in coding and decoding a moving picture, it is difficult to reduce the blocking artifact with a small operation capacity.
Consequently, the related art methods involve various problems and disadvantages when reducing a blocking artifact created in coding a moving picture. A calculation for performing an algorithm is complicated, and the calculation amount and time become correspondingly large. Further, the blocking artifacts are not reduced in either complex regions or smooth regions in a picture. In addition, the amount of bits to be transmitted increases.
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